State of Charge Estimation of Lithium-Ion Batteries Using LSTM and NARX Neural Networks
TL;DR: A novel machine-learning method to address the risk of gradient explosion and gradient decent using the dynamic nonlinear auto-regressive models with exogenous input neural network (NARX) with long short-term memories (LSTM) with jump-ahead connections in the time-unfolded model.
read more
Abstract: Highly accurate state of charge (SOC) estimation of lithium-ion batteries is one of the key technologies of battery management systems in electric vehicles. The performance of SOC estimation directly influences the driving range and safety of these vehicles. Due to external disturbances, temperature variation and electromagnetic interference, accurate SOC estimation becomes difficult. To accurately estimate the SOC of lithium-ion batteries, this article presents a novel machine-learning method to address the risk of gradient explosion and gradient decent using the dynamic nonlinear auto-regressive models with exogenous input neural network (NARX) with long short-term memories (LSTM).The proposed hybrid NARX model embeds LSTM memory, which provides jump-ahead connections in the time-unfolded model. These jump-ahead connections provide a shorter path for the propagation of gradient information, therefore reducing long-term dependence on the recurrent neural network. Experimental results show that the estimation performance root mean square error (RMSE) of the proposed model is less than 1%, and this model has better multitime prediction performance. Finally, the hybrid NARX and LSTM model is compared with the standard back propagation neural network based on particle swarm optimization (BPNN-PSO), the least-squares support vector machine (LS-SVM) and LSTM existing models under urban dynamometer driving schedule (UDDS) and dynamic stress test (DST) conditions. The proposed hybrid NARX-LSTM model yield relative to other methods and can estimate the battery SOC with high accuracy. The RMSE of proposed model is improved by approximately 60% compared with the standard LSTM
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
Artificial Neural Networks, Gradient Boosting and Support Vector Machines for electric vehicle battery state estimation: A review
TL;DR: In this article , a review of the commonly proposed Artificial Intelligence data-driven based state of charge and state of health estimation of Li-ion batteries for electric vehicles has been presented, with the goal of increasing the feasibility of implementing Artificial Intelligence based battery state estimation in electric vehicles.
169
Deep learning approach towards accurate state of charge estimation for lithium-ion batteries using self-supervised transformer model.
Mahammad A. Hannan,Dickson Neoh Tze How,M. S. Hossain Lipu,Muhamad Mansor,Pin Jern Ker,Zhao Yang Dong,Khairul Salleh Mohamed Sahari,Sieh Kiong Tiong,Kashem M. Muttaqi,T. M. Indra Mahlia,T. M. Indra Mahlia,Frede Blaabjerg +11 more
Abstract: Accurate state of charge (SOC) estimation of lithium-ion (Li-ion) batteries is crucial in prolonging cell lifespan and ensuring its safe operation for electric vehicle applications. In this article, we propose the deep learning-based transformer model trained with self-supervised learning (SSL) for end-to-end SOC estimation without the requirements of feature engineering or adaptive filtering. We demonstrate that with the SSL framework, the proposed deep learning transformer model achieves the lowest root-mean-square-error (RMSE) of 0.90% and a mean-absolute-error (MAE) of 0.44% at constant ambient temperature, and RMSE of 1.19% and a MAE of 0.7% at varying ambient temperature. With SSL, the proposed model can be trained with as few as 5 epochs using only 20% of the total training data and still achieves less than 1.9% RMSE on the test data. Finally, we also demonstrate that the learning weights during the SSL training can be transferred to a new Li-ion cell with different chemistry and still achieve on-par performance compared to the models trained from scratch on the new cell.
A review of lithium-ion battery state of charge estimation based on deep learning: Directions for improvement and future trends
TL;DR: In this paper , a review of state-of-charge estimation methods based on deep learning is presented, and the results of the estimation methods are analyzed and summarized in terms of feature engineering, data augmentation, learning rate strategies, optimization functions and optimal hyper-parameters.
100
A Comprehensive Review of Lithium-Ion Batteries Modeling, and State of Health and Remaining Useful Lifetime Prediction
01 Jan 2022
TL;DR: In this paper , Li-ion battery state-of-health (SOH) and remaining useful life (RUL) estimation along with a discussion of their advantages and limitations are presented.
67
Battery monitoring and prognostics optimization techniques: Challenges and opportunities
TL;DR: In this paper , the state-of-the-art in battery health monitoring and prognostics is reviewed and discussed based on the dimensions and the criteria defined in the framework.
59
References
Data-driven prediction of battery cycle life before capacity degradation
Kristen A. Severson,Peter M. Attia,Norman Jin,Nicholas Perkins,Benben Jiang,Zi Yang,Michael H. Chen,Muratahan Aykol,Patrick Herring,Dimitrios Fraggedakis,Martin Z. Bazant,Stephen J. Harris,Stephen J. Harris,William C. Chueh,Richard D. Braatz +14 more
TL;DR: In this article, a machine learning method was used to predict battery lifetime before the onset of capacity degradation with high accuracy. But, the prediction often cannot be made unless a battery has already degraded significantly.
Long Short-Term Memory Recurrent Neural Network for Remaining Useful Life Prediction of Lithium-Ion Batteries
TL;DR: The developed method is able to predict the battery's RUL independent of offline training data, and when some offline data is available, the RUL can be predicted earlier than in the traditional methods.
1K
State of Charge Estimation for Lithium-Ion Batteries Using Model-Based and Data-Driven Methods: A Review
TL;DR: This review presents the recent SOC estimation methods highlighting the model-based and data-driven approaches and delivers potential recommendations for the development of SOC estimation method of lithium-ion battery in EV applications.
567
Overview of model-based online state-of-charge estimation using Kalman filter family for lithium-ion batteries
TL;DR: Challenge steps in the implementation of KF family algorithms in model-based online SOC estimation processes, such as selection of battery model, initial SOC and filter tuning, are elaborated for the efficient development of a battery management system, especially for EV application.
549
State-of-charge estimation of lithium-ion batteries based on gated recurrent neural network
TL;DR: A recurrent neural network with gated recurrent unit is proposed to estimate the battery SOC from measured current, voltage, and temperature signals, which exploits information of the previous SOCs and measurements and yields better estimation accuracy.
426